Hard Negative Generation In contrast to mining-based methods, generation-based methods [1]}, [2]}, [3]} try to exploit all the training samples by extracting the semantic information required to generate synthetic samples which act as hard negatives. Deep adversarial metric learning (DAML) [1]} trains the embedding network and the hard negative generator network in an adversarial manner to generate hard negatives. In hardness-aware deep metric learning (HDML) [2]}, synthetic samples are created via interpolation in the embedding space. This is followed by finding the corresponding label-preserving mappings in the feature space using an autoencoder. They also control the level of hardness while training. Although these methods utilize all the training samples, they require an additional network as the generator. This increases the training time and computational load and can also lead to optimization difficulty [6]}.
